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AI-generated unit tests suck. Claude overshoots. GPT-5 is lazy af. No model has proper common sense. I tinkered around for a long time to get GitHub Copilot to actually work as expected. The result is this workflow.

1. The AI figures out business logic & edge cases and then proposes tests based on that. 2. You approve or modify the list. 3. Then Copilot implements everything autonomously while you do whatever. It's a little bit more complicated than that but that's the gist of it.

It uses many of GitHub Copilot's capabilities (instruction files, prompt files, some tools & configs) for prompt and context engineering. With that, it's reliably able to one-shot entire test suites in my day-to-day work. See for yourself (select 2x speed):